A practical guide to estimating the light extinction coefficient with nonlinear models—a case study on maize

Autores
Lacasa, Josefina; Hefley, Trevor J.; Otegui, María Elena; Ciampitti, Ignacio A.
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The fraction of intercepted photosynthetically active radiation (fPARi) is typically described with a non-linear function of leaf area index (LAI) and k, the light extinction coefficient. The parameter k is used to make statistical inference, as an input into crop models, and for phenotyping. It may be estimated using a variety of statistical techniques that differ in assumptions, which ultimately influences the numerical value k and associated uncertainty estimates. A systematic search of peer-reviewed publications for maize (Zea Mays L.) revealed: (i) incompleteness in reported estimation techniques; and (ii) that most studies relied on dated techniques with unrealistic assumptions, such as log-transformed linear models (LogTLM) or normally distributed data. These findings suggest that knowledge of the variety and trade-offs among statistical estimation techniques is lacking, which hinders the use of modern approaches such as Bayesian estimation (BE) and techniques with appropriate assumptions, e.g. assuming beta-distributed data.
Estación Experimental Agropecuaria Pergamino
Fil: Lacasa, Josefina. Kansas State University. Department of Agronomy; Estados Unidos
Fil: Lacasa, Josefina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Hefley, Trevor J. Kansas State University. Department of Statistics; Estados Unidos
Fil: Otegui, María E. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; Argentina
Fil: Otegui, María E. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Otegui, María E. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ciampitti , Ignacio A. Kansas State University. Department of Agronomy; Estados Unidos
Fuente
Plant Methods 17 : 60 (2021)
Materia
Radiation
Maize
Statistical Sampling
Radiación
Maíz
Zea mays
Muestreo Estadístico
Nonlinear Models
Modelos No Lineales
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/9723
Acceder
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oai_identifier_str oai:localhost:20.500.12123/9723
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling A practical guide to estimating the light extinction coefficient with nonlinear models—a case study on maizeLacasa, JosefinaHefley, Trevor J.Otegui, María ElenaCiampitti, Ignacio A.RadiationMaizeStatistical SamplingRadiaciónMaízZea maysMuestreo EstadísticoNonlinear ModelsModelos No LinealesThe fraction of intercepted photosynthetically active radiation (fPARi) is typically described with a non-linear function of leaf area index (LAI) and k, the light extinction coefficient. The parameter k is used to make statistical inference, as an input into crop models, and for phenotyping. It may be estimated using a variety of statistical techniques that differ in assumptions, which ultimately influences the numerical value k and associated uncertainty estimates. A systematic search of peer-reviewed publications for maize (Zea Mays L.) revealed: (i) incompleteness in reported estimation techniques; and (ii) that most studies relied on dated techniques with unrealistic assumptions, such as log-transformed linear models (LogTLM) or normally distributed data. These findings suggest that knowledge of the variety and trade-offs among statistical estimation techniques is lacking, which hinders the use of modern approaches such as Bayesian estimation (BE) and techniques with appropriate assumptions, e.g. assuming beta-distributed data.Estación Experimental Agropecuaria PergaminoFil: Lacasa, Josefina. Kansas State University. Department of Agronomy; Estados UnidosFil: Lacasa, Josefina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; ArgentinaFil: Hefley, Trevor J. Kansas State University. Department of Statistics; Estados UnidosFil: Otegui, María E. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Otegui, María E. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; ArgentinaFil: Otegui, María E. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ciampitti , Ignacio A. Kansas State University. Department of Agronomy; Estados UnidosSpringer Nature2021-07-02T16:04:56Z2021-07-02T16:04:56Z2021-06-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/9723https://plantmethods.biomedcentral.com/articles/10.1186/s13007-021-00753-21746-4811https://doi.org/10.1186/s13007-021-00753-2Plant Methods 17 : 60 (2021)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repograntAgreement/INTA/PNCYO-1127042/AR./Bases ecofisiológicas para el mejoramiento genético y la calidad diferenciada de cereales y oleaginosas.info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-29T13:45:16Zoai:localhost:20.500.12123/9723instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:45:16.725INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv A practical guide to estimating the light extinction coefficient with nonlinear models—a case study on maize
title A practical guide to estimating the light extinction coefficient with nonlinear models—a case study on maize
spellingShingle A practical guide to estimating the light extinction coefficient with nonlinear models—a case study on maize
Lacasa, Josefina
Radiation
Maize
Statistical Sampling
Radiación
Maíz
Zea mays
Muestreo Estadístico
Nonlinear Models
Modelos No Lineales
title_short A practical guide to estimating the light extinction coefficient with nonlinear models—a case study on maize
title_full A practical guide to estimating the light extinction coefficient with nonlinear models—a case study on maize
title_fullStr A practical guide to estimating the light extinction coefficient with nonlinear models—a case study on maize
title_full_unstemmed A practical guide to estimating the light extinction coefficient with nonlinear models—a case study on maize
title_sort A practical guide to estimating the light extinction coefficient with nonlinear models—a case study on maize
dc.creator.none.fl_str_mv Lacasa, Josefina
Hefley, Trevor J.
Otegui, María Elena
Ciampitti, Ignacio A.
author Lacasa, Josefina
author_facet Lacasa, Josefina
Hefley, Trevor J.
Otegui, María Elena
Ciampitti, Ignacio A.
author_role author
author2 Hefley, Trevor J.
Otegui, María Elena
Ciampitti, Ignacio A.
author2_role author
author
author
dc.subject.none.fl_str_mv Radiation
Maize
Statistical Sampling
Radiación
Maíz
Zea mays
Muestreo Estadístico
Nonlinear Models
Modelos No Lineales
topic Radiation
Maize
Statistical Sampling
Radiación
Maíz
Zea mays
Muestreo Estadístico
Nonlinear Models
Modelos No Lineales
dc.description.none.fl_txt_mv The fraction of intercepted photosynthetically active radiation (fPARi) is typically described with a non-linear function of leaf area index (LAI) and k, the light extinction coefficient. The parameter k is used to make statistical inference, as an input into crop models, and for phenotyping. It may be estimated using a variety of statistical techniques that differ in assumptions, which ultimately influences the numerical value k and associated uncertainty estimates. A systematic search of peer-reviewed publications for maize (Zea Mays L.) revealed: (i) incompleteness in reported estimation techniques; and (ii) that most studies relied on dated techniques with unrealistic assumptions, such as log-transformed linear models (LogTLM) or normally distributed data. These findings suggest that knowledge of the variety and trade-offs among statistical estimation techniques is lacking, which hinders the use of modern approaches such as Bayesian estimation (BE) and techniques with appropriate assumptions, e.g. assuming beta-distributed data.
Estación Experimental Agropecuaria Pergamino
Fil: Lacasa, Josefina. Kansas State University. Department of Agronomy; Estados Unidos
Fil: Lacasa, Josefina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Hefley, Trevor J. Kansas State University. Department of Statistics; Estados Unidos
Fil: Otegui, María E. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; Argentina
Fil: Otegui, María E. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Otegui, María E. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ciampitti , Ignacio A. Kansas State University. Department of Agronomy; Estados Unidos
description The fraction of intercepted photosynthetically active radiation (fPARi) is typically described with a non-linear function of leaf area index (LAI) and k, the light extinction coefficient. The parameter k is used to make statistical inference, as an input into crop models, and for phenotyping. It may be estimated using a variety of statistical techniques that differ in assumptions, which ultimately influences the numerical value k and associated uncertainty estimates. A systematic search of peer-reviewed publications for maize (Zea Mays L.) revealed: (i) incompleteness in reported estimation techniques; and (ii) that most studies relied on dated techniques with unrealistic assumptions, such as log-transformed linear models (LogTLM) or normally distributed data. These findings suggest that knowledge of the variety and trade-offs among statistical estimation techniques is lacking, which hinders the use of modern approaches such as Bayesian estimation (BE) and techniques with appropriate assumptions, e.g. assuming beta-distributed data.
publishDate 2021
dc.date.none.fl_str_mv 2021-07-02T16:04:56Z
2021-07-02T16:04:56Z
2021-06-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/9723
https://plantmethods.biomedcentral.com/articles/10.1186/s13007-021-00753-2
1746-4811
https://doi.org/10.1186/s13007-021-00753-2
url http://hdl.handle.net/20.500.12123/9723
https://plantmethods.biomedcentral.com/articles/10.1186/s13007-021-00753-2
https://doi.org/10.1186/s13007-021-00753-2
identifier_str_mv 1746-4811
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repograntAgreement/INTA/PNCYO-1127042/AR./Bases ecofisiológicas para el mejoramiento genético y la calidad diferenciada de cereales y oleaginosas.
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv Plant Methods 17 : 60 (2021)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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